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APPLICATION OF WIRELESS SENSOR NETWORKS FOR
ENVIRONMENTAL MONITORING AND DEVELOPMENT
OF AN ENERGY EFFICIENT CLUSTER BASED ROUTING
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THEREQUIREMENTS FOR THE DEGREE OF
Master of TechnologyIn
Electrical Engineering
By
ROHIT VAISH
Under the guidance of
Prof. J.K. SATAPATHY
Department of Electrical Engineering
National Institute of Technology
Rourkela 2008-2009
Application of Wireless Sensor Networks forEnvironmental Monitoring & Development of
an Energy Efficient Hierarchical Clusterbased Routing
A Thesis Submitted in Partial Fulfillment for the Award of the Degree
Of
MASTER OF TECHNOLOGYIn
ELECTRICAL ENGINEERING
By
Rohit Vaish
Electrical Engineering DepartmentNational Institute of Technology
Rourkela 769008
2008-2009
National Institute of Technology
Rourkela
CERTIFICATE
This is to certify that the thesis entitled Application of Wireless Sensor
Networks for Environmental Monitoring & Development of an Energy Efficient
Hierarchical Cluster based Routing submitted by Mr. ROHIT VAISH in partial
fulfillment of the requirements for the award of Master of Technology Degree in
Electrical Engineering with specialization in “Electronics System &
Communication” at the National Institute of Technology, Rourkela (Deemed
University) is an authentic work carried out by him under my supervision and
guidance.
To the best of my knowledge, the matter embodied in the thesis has not been
submitted to any other University/Institute for the award of any Degree or Diploma.
Prof. J.K. SATAPATHYNational Institute of Technology
Rourkela-769008
ACKNOWLEDGEMENT
I would like to express my deep sense of profound gratitude to my honorable, esteemed
guide, Prof. J.K. SATAPATHY for his guidance and constant support. Over the time he has
introduced me to the academic world. His perspective on my work has inspired me to go on. I
am glad to work with him.
I extend my thanks to our HOD, Dr. B.D.SUBUDHI for his valuable advices and
encouragement.
I would like to thank all my friends and especially ESC friends for all the thoughtful and
mind stimulating discussions we had, which prompted us to think beyond the obvious.
I cannot end without thanking my lovely family, on whose encouragement, support, and love,
I have relied throughout my studies.
I would like to thank to all those who are directly or indirectly supported me in carrying out
this thesis work successfully.
ROHIT VAISHRoll No. 207EE106Department of Electrical Engg.
i
CONTENTS
PAGE NO.
DECLARATIONACKNOWLEDGEMENTTABLE OF CONTENTS iABSTRACT vLIST OF FIGURES viLIST OF TABLES viii
1. INTRODUCTION TO WIRELESS SENSOR NETWORKS
1.1 Introduction 1
1.2 Wireless Sensor Network vs. Ad hoc Network 3
1.3 Clustering in WSN (Wireless Sensor Network) 4
1.4 Energy Advantages 4
1.5 Detection advantage 5
1.6 Sensor Network Applications 5
1.7 Motivation of the Work 6
1.8 Objective of the Thesis 7
2. LITERATURE SURVEY
2.1 Introduction 8
2.2 Evolution of Sensor Nodes 8
2.3 Military Networks of Sensors 9
2.4 Next Generation Wireless Sensor Nodes 9
2.4.1 WINS from UCLA 9
2.4.2 Motes from UC Berkeley 10
2.4.3 Medusa from UCLA 10
2.5 Why Microscopic Sensor Nodes? 11
ii
3. SOFTWARE AND PROGRAMMING DESCRIPTION3.1 Introduction 13
3.1.1 MoteConfig 13
3.2 Installation 13
3.2.1 Supported Platforms 13
3.3 Installing MoteView on a Windows PC 13
3.3.1 Installation Steps 14
3.4 PC Interface Port Requirements 14
3.5 Installation Steps for MoteConfig 15
3.6 Starting MoteConfig 16
3.7 Local Programming 17
3.8 Settings 18
3.9 Programming 18
3.10 MoteView 22
3.10.1 MoteView Overview 22
3.11 Supported Sensor Boards and Mote Platforms 23
3.12 Application Quick Start 25
3.12.1 Connecting to a Live Sensor Network on your local PC 25
3.13 Visualization Tabs 28
3.13.1 Data 28
3.14 Summary of the Process How to get live Data through Sensors 29
4. SENSOR KIT DETAILS ( HARDWARE DESCRIPTION)
4.1 Introduction 31
4.2 MPR2400 (MICAz) 31
4.2.1 Product Summary 31
4.2.2 Block Diagram and Schematics for the MPR2400 / MICAz 32
4.2.3 51-pin Expansion Connector 33
4.2.4 CC2420 Radio 34
iii
4.3 Data Acquisition Boards 35
4.4 MDA100CA/MDA100CB 35
4.4.1 Thermistor 36
4.4.2 Conversion to Engineering Units 36
4.4.3 Light Sensor 37
4.4.4 Prototyping Area 37
4.5 MIB520 USB Interface Board 39
4.5.1 ISP (In System Processor) 40
4.5.2 Mote Programming Using the MIB520 40
4.6 MIB520 Use 40
4.6.1 Install FTDI USB Virtual COM Port Drivers 40
4.6.2 Reset 41
4.6.3 JTAG 41
4.6.4 Power 41
4.6.5 USB Interface 41
5. FUNDAMENTALS OF CLUSTER BASED ROUTING
5.1 Introduction 42
5.2 Statesofasensornode 42
5.3 Election Phase 43
5.4 DataTransfer Phase 43
5.5 Quantitative Analysis 44
5.5.1 Radio Communication Model 44
5.5.2 Election Phase 45
5.5.3 Data Transfer Phase 46
iv
6. RESULTS AND DISCUSSION 48
7. CONCLUSION AND FUTURE WORK 58
REFERENCES 59
v
ABSTRACT
Wireless Sensor Networks (WSNs) have attracted the attention of many researchers.
Wireless Sensor Networks (WSNs) are used for various applications such as habitat
monitoring, automation, agriculture, and security. Since numerous sensors are usually
deployed on remote and inaccessible places, the deployment and maintenance should be easy
and scalable. Wireless sensor network consists of large number of small nodes. The nodes
then sense environmental changes and report them to other nodes over flexible network
architecture. Sensor nodes are great for deployment in hostile environments or over large
geographical areas.
The measurement of temperature & light by the use of Crossbow sensor kit in which
there are different nodes/motes placed at different locations. These nodes are having different
node identification & they will sense the temperature & light of there surrounding location
and send it to the base station node which is connected through USB port to the computer by
the use of MoteView & MoteConfig environment. The data acquisition board that we have
used is MDA100CB (Mote Data Acquisition). The programming of the sensor nodes is done
by MoteConfig & live data is viewed through MoteView environment. The nodes that we
have used are MicaZ, the MDA100CB board is fixed over these nodes by means of 51
Input/output pins.
An energy efficient hierarchical cluster-based routing protocol for continuous stream
queries in WSN. We introduce a set of cluster heads, head-set, for cluster-based routing. The
head-set members are responsible for control and management of the network. On rotation
basis, a head-set member receives data from the neighboring nodes and transmits the
aggregated results to the distant base station. For a given number of data collecting sensor
nodes, the number of control and management nodes can be systematically adjusted to reduce
the energy consumption, which increases the network life. Nodes in a sensor network are
severely constrained by energy, storage capacity and computing power. To prolong the
lifetime of the sensor nodes, designing efficient routing protocols is critical.
vi
LIST OF FIGURES Page No.
Figure 3.1 MoteConfig 2.0 and OTAP MoteWorks Installer 16
Figure 3.2 MoteConfig Application GUI 17
Figure 3.3 MIB520 Gateway Settings 18
Figure 3.4 File Browser for selecting XMesh applications 19
Figure 3.5 Binary Scan Result of an XMeshBase application 20
Figure 3.6 MoteConfig programming in progress 21
Figure 3.7 MoteConfig programming successful 21
Figure 3.8 Connect to WSN (Mode) 25
Figure 3.9 Connect to WSN (Gateway) 26
Figure 3.10 Connect to WSN (Serial port) 26
Figure 3.11 Connect to WSN ( Port) 27
Figure 3.12 Connect to WSN (Sensor board) 28
Figure 3.13 Screenshot of a demo database displayed in the Data tab 29
Figure 4.1 Photo of the MPR2400 MICAz with standard antenna 31
Figure 4.2 Block Diagram for the MPR2400 / MICAz 32
Figure 4.3 51-pin Expansion Connector 33
Figure 4.4 CC2420 Radio 34
Figure 4.5 MDA100CB 39
Figure 4.6 Photo of top view of an MIB520CA 39
Figure 4.7 Photo of top view of an MIB520CB 40
Figure 6.1 Showing temperature and light measurement 48
Figure 6.2 Showing temperature readings in topology form 49
Figure6.3 Showing light readings in topology form 50
Figure 6.4 Showing voltage readings in topology form 51
Figure 6.5 Optimum number of cluster Vs Head set size 52
vii
Figure 6.6 Variation in maximum cluster size with respect to distance from the
base station and the head-set size. 53
Figure 6.7 The energy consumption with respect to the number of clusters 54
Figure 6.8 Energy consumed per round with respect to number of clusters 55
Figure 6.9 Energy consumed per round with respect to head-set size and network
diameter. 56
Figure 6.10 The time for iteration with respect to cluster diameter and the head-set size. 57
viii
LIST OF TABLES
Page No.
Table 3.1 Pre-compiled MICAz XMesh applications .15
Table 3.2 Mote processor/radio (MPR) platforms supported by MoteView ..23
Table 3.3 Sensor (MTS series) and data acquisition boards supported by MoteView
and their plug-and-play compatible Mote platforms .. 24
Table 4.1 Crossbow s Sensor and Data Acquisition Boards ..35
Table 4.2 Resistance vs. Temperature 36
Table 4.3 Connection Table for MDA100. Use the photo (top view) below the table
to locate the pins ....38
Table 5.1 Sample parameter values of the radio communication model used in
our quantitative analysis .46
INTRODUCTION TO WIRELESS SENSOR NETWORKS
INTRODUCTION TO WIRELESS SENSOR NETWORKS
1
1.1 Introduction
Wireless sensor networks have recently come into prominence because they hold the
potential to revolutionize many segments of our economy and life, from environmental
monitoring and conservation, to manufacturing and business asset management, to
automation in the transportation and health care industries. The design, implementation, and
operation of a sensor network requires the confluence of many disciplines, including signal
processing, networking and protocols, embedded systems, information management and
distributed algorithms. Such networks are often deployed in resource-constrained
environments, for instance with battery operated nodes running untethered. These constraints
dictate that sensor network problems are best approached in a hostile manner, by jointly
considering the physical, networking, and application layers and making major design trade-
offs across the layers.
Advances in wireless networking, micro-fabrication and integration (for examples,
sensors and actuators manufactured using micro-electromechanical system technology, or
MEMS), and embedded microprocessors have enabled a new generation of massive-scale
sensor networks suitable for a range of commercial and military applications. The technology
promises to revolutionize the way we live, work, and interact with the physical environment.
In a typical sensor network, each sensor node operates untethered and has a microprocessor
and a small amount of memory for signal processing and task scheduling. Each node is
equipped with one or more sensing devices such as acoustic microphone arrays, video or still
cameras, infrared (IR), seismic, or magnetic sensors. Each sensor node communicates
wirelessly with a few other local nodes within its radio communication range.
Sensor networks extend the existing Internet deep into the physical environment. The
resulting new network is orders of magnitude more expansive and dynamic than the current
TCP/IP networks and is creating entirely new types of traffic that are quite different from
what one finds on the Internet now. Information collected by and transmitted on a sensor
network describes conditions of physical environments for example, temperature, humidity,
or vibration and requires advanced query interfaces and search engines to effectively support
user-level functions. Sensor networks may inter-network with an IP core network via a
number of gateways. A gateway routes user queries or commands to appropriate nodes in a
sensor network. It also routes sensor data, at times aggregated and summarized, to users who
have requested it or are expected to utilize the information. A data repository or storage
service may be present at the gateway, in addition to data logging at each sensor. The
INTRODUCTION TO WIRELESS SENSOR NETWORKS
2
repository may serve as an intermediary between users and sensors, providing a persistent
data storage. It is well known that communicating 1 bit over the wireless medium at short
ranges consumes far more energy than processing that bit.
The information management and networking for sensor networks will require more than
just building faster routers, switchers, and browsers. A sensor network is designed to collect
information from a physical environment. In many applications, it is more appropriate to
address nodes in a sensor network by physical properties, such as node locations or
proximity, than by IP addresses. How and where data is generated by sensors and consumed
by users will affect the way data is compressed, routed, and aggregated. Because of the peer-
to-peer connectivity and the lack of a global infrastructure support, the sensors have to rely
on discovery protocols to construct local models about the network and environment.
Wireless sensor networks are a trend of the past few years, and they involve deploying a
large number of small nodes. The nodes then sense environmental changes and report them to
other nodes over a flexible network architecture. Sensor nodes are great for deployment in
hostile environments or over large geographical areas. The sensor nodes leverage the strength
of collaborative efforts to provide higher quality sensing in time and space as compared to
traditional stationary sensors, which are deployed in the following two ways:
• Sensors can be positioned far from the actual phenomenon, i.e. something known by
sense perception. In this approach, large sensors that use some complex techniques to
distinguish the targets from environmental noise are required.
• Several sensors that perform only sensing can be deployed. The position of the
sensors and communications topology is carefully engineered. They transmit time
series of the sensed phenomenon to central nodes where computations are performed
and data are fused.
A wireless sensor network is a collection of nodes organized into a cooperative network.
Each node consists of processing capability (one or more microcontrollers, CPUs or DSP
chips), may contain multiple types of memory (program, data and flash memories), have a RF
transceiver (usually with a single Omni-directional antenna), have a power source (e.g.,
batteries and solar cells), and accommodate various sensors and actuators. The nodes
communicate wirelessly and often self-organize after being deployed in an ad hoc fashion.
Currently, wireless sensor networks are beginning to be deployed at an accelerated pace. It is
not unreasonable to expect that in 10-15 years that the world will be covered with wireless
sensor networks with access to them via the Internet. This can be considered as the Internet
INTRODUCTION TO WIRELESS SENSOR NETWORKS
3
becoming a physical network. Wireless Sensor Network is widely used in electronics. This
new technology is exciting with unlimited potential for numerous application areas including
environmental, medical, military, transportation, entertainment, home automation and traffic
control crisis management, homeland defense, and smart spaces.
1.2 Wireless Sensor Network vs. Ad hoc Network
A mobile ad hoc network (MANET), sometimes called a mobile mesh network, is a self-
configuring network of mobile devices connected by wireless links. Each device in a
MANET is free to move independently in any direction, and will therefore change its links to
other devices frequently. The difference between wireless sensor networks and ad-hoc
networks are outlined below:
• The number of sensor nodes in a sensor network can be several orders of magnitude
higher than the nodes in an ad hoc network.
• Sensor nodes are densely deployed.
• Sensor nodes are prone to failures.
• The topology of a sensor network changes very frequently.
• Sensor nodes mainly use broadcast communication paradigm whereas most ad hoc
networks are based on point-to-point communication.
• Sensor nodes are limited in power, computational capacities, and memory.
• Sensor nodes may not have global identification (ID) because of the large amount of
overheads and large number of sensors.
• Sensor networks are deployed with a specific sensing application in mind whereas ad-
hoc networks are mostly constructed for communication purpose.
To summarize, the challenges we face in designing sensor network systems and
applications include:-
1. Limited hardware: Each node has limited processing, storage, and communication
capabilities, and limited energy supply and bandwidth.
2. Limited support for networking: The network is peer-to-peer, with a mesh topology
and dynamic, mobile, and unreliable connectivity. There are no universal routing
protocols or central registry services.
3. Limited support for software development: The tasks are typically real-time and
massively distributed, involve dynamic collaboration among nodes, and must handle
INTRODUCTION TO WIRELESS SENSOR NETWORKS
4
multiple competing events. Global properties can be specified only via local
instructions. Because of the coupling between applications and system layers, the
software architecture must be codesigned with the information processing
architecture.
1.3 Clustering in WSN (Wireless Sensor Network)It is widely accepted that the energy consumed in one bit of data transfer can be used
to perform a large number of arithmetic operations in the sensor processor. Moreover in a
densely deployed sensor network the physical environment would produce very similar data
in near-by sensor nodes and transmitting such data is more or less redundant. Therefore, all
these facts encourage using some kind of grouping of nodes such that data from sensor nodes
of a group can be combined or compressed together in an intelligent way and transmit only
compact data. This can not only reduce the global data to be transmitted and localized most
traffic to within each individual group, but reduces the traffic and hence contention in a
wireless sensor network. This process of grouping of sensor nodes in a densely deployed
large-scale sensor network is known as clustering. The intelligent way to combined and
compress the data belonging to a single cluster is known as data aggregation.
There are some issues involved with the process of clustering in a wireless sensor
network. First issue is, how many clusters should be formed that could optimize some
performance parameter. Second could be how many nodes should be taken into a single
cluster. Third important issue is the selection procedure of cluster-head in a cluster. Another
issue that has been focused in many research papers is to introduce heterogeneity in the
network. It means
that user can put some more powerful nodes, in terms of energy, in the network which can act
as a cluster-head and other simple node work as cluster-member only. Considering the above
issues, many protocols have been proposed which deals with each individual issue.
1.4 Energy Advantages
Because of the unique attenuation characteristics of radio-frequency (RF) signals, a
multihop RF network provides a significant energy saving over a single-hop network for the
same distance. Consider the simple example of an N-hop network. Assume the overall
distance of transmission is Nr, where r is the one-hop distance. The minimum receiving
INTRODUCTION TO WIRELESS SENSOR NETWORKS
5
power at a node for a given transmission error rate is Preceive , and the power at a transmission
node is Psend. Then, the RF attenuation model near the ground is given by
rsendP
receiveP (1.1)
where r is the transmission distance and is the RF attenuation exponent. Due to multipath
and other interference effects, is typically in the range of 2 to 5. Equivalently,
Psend r .Preceive (1.2)
Therefore, the power advantage of an N-hop transmission versus a single-hop
transmission over the same distance Nr is
1N(r)sendN.P
(Nr)sendPrf
−== (1.3)
A larger N gives a larger power saving due to the consideration of RF energy alone.
However, this analysis ignores the power usage by other components of an RF circuitry.
Using more nodes not only increases the cost, but also the power consumption of these
other RF components. In practice, an optimal design seeks to balance the two conflicting
factors for an overall cost and energy efficiency.
1.5 Detection advantageEach sensor has a finite sensing range, determined by the noise floor at the sensor. A
denser field improves the odds of detecting a signal source within the range. Once a signal
source is inside the sensing range of a sensor, further increasing the sensor density decreases
the average distance from a sensor to the signal source, hence improving the signal-to-noise
ratio (SNR).
1.6 Sensor Network ApplicationsA sensor network is designed to perform a set of high-level information processing tasks
such as detection, tracking, or classification. Measures of performance for these tasks are well
defined, including detection of false alarms or misses, classification errors, and track quality.
Applications of sensor networks are wide ranging and can vary significantly in application
requirements, mode of deployment (e.g., ad hoc versus instrumented environment), sensing
INTRODUCTION TO WIRELESS SENSOR NETWORKS
6
modality, or means of power supply (e.g. battery versus wall socket). Sample commercial and
military applications include:
• Environmental monitoring (e.g. traffic, habitat, security)
• Industrial sensing and diagnostics (e.g. appliances, factory, supply chains)
• Infrastructure protection (e.g. power grid, water distribution)
• Battlefield awareness (e.g. mutitarget tracking)
• Context-aware computing (e.g. intelligent home, responsive environment)
1.7 Motivation of the WorkCurrent research in the areas of wireless communications, micro-electromechanical
systems and low power design is progressively leading to the development of cost effective,
energy efficient, multifunctional sensor nodes. Sensing, communication, processing and
battery units are the primary components of a sensor node. Individual sensors have the
capacity to detect events occurring in their area of deployment.
Reliable data transport is an important facet of dependability and quality of service in
several applications of wireless sensor networks. Different applications have different
reliability requirements, for example an application to collect environmental parameters like
temperature, humidity etc periodically can ignore an occasional loss of a value from a
particular sensor but for an application in which the data collected by every sensor is a critical
piece of information then end-to-end reliability has to be guaranteed for every individual
packet.
Routing protocols providing an optimal data transmission route from sensor nodes to
sink to save energy of nodes in the network. Data aggregation plays an important role in
energy conservation of sensor network. Data aggregation methods are used not only for
finding an optimal path from source to destination but also to eliminate the redundancy of
data, since transmitting huge volume of raw data is an energy intensive operation, and thus
minimizing the number of data transmission. Also multiple sensors may sense the same
phenomenon, although from different view and if this data can be reconciled into a more
meaningful form as it passes through the network, it becomes more useful to an application.
An example for an application that requires guaranteed end-to-end reliability is an
integration of Radio Frequency Identification (RFID) and wireless sensor network for
automated inventory management and tracking. In this application setup the sensor devices
called motes are attached with RFID readers to record RFID tag information on the objects.
INTRODUCTION TO WIRELESS SENSOR NETWORKS
7
These sensor motes have a critical piece of information to be sent to the sink. Therefore
reliable sensor-to-sink communication has to be guaranteed for such applications. This is the
main motivation behind studying the various issues and strategies of reliable communication
in this thesis.
1.8 Objective of the Thesis
• To measure or sense temperature and light parameters through Crossbow Sensor Kit
by using MoteView and MoteConfig environment.
• The energy efficient hierarchical cluster-based routing for Wireless Sensor Networks
(WSN).
LITERATURE SURVEY
LITERATURE SURVEY
8
2.1 Introduction
Researchers have focused on Wireless Sensor challenges that have limited resource
capabilities of the hardware i.e. memory, processing power, bandwidth and energy deposits.
Much research is currently being conducted in the following areas:
Increasing network lifetime.
Improving reliability of data transfer.
Finding solutions to assist easy deployment and maintenance.
Developing techniques that will enforce secure, private and trustworthy networks.
A wireless sensor network (WSN) has important applications such as remote
environmental monitoring and target tracking. This has been enabled by the availability,
particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors
are equipped with wireless interfaces with which they can communicate with one another to
form a network. The design of a WSN depends significantly on the application, and it must
consider factors such as the environment, the application s design objectives, cost, hardware,
and system constraints. We give an overview of several new applications and then review the
literature on various aspects of WSNs.
A sensor network system consisting of a large number of small sensors with low-
power can be an effective tool for collection and integration of data by each sensor in a
variety of environments. The collected data by each sensor node is communicated through the
network to a single base station that uses all collected data to determine properties of the data.
Clustering sensors into groups, yields that sensors communicate information only to cluster
heads and then the clusterheads communicate the aggregated information to the base station.
We estimate the optimal number of cluster-heads among randomized sensors in a bounded
region. The algorithm minimize the total energy spent in the wireless sensor network when
all sensors communicate data from the cluster-heads to the base station.
2.2 Evolution of Sensor Nodes
There has been a long history for (remote) sensing as a means for humans to observe
the physical world. For example, the telescope invented in the 16th century is simply a device
LITERATURE SURVEY
9
for viewing distant objects. As with many technologies, the development of sensor networks
has been largely driven by defense applications.
2.3 Military Networks of Sensors
Since the early 1950s, a system of long-range acoustic sensors (hydrophones), called
the Sound Surveillance System (SOSUS), has been deployed in the deep basins of the
Atlantic and Pacific oceans for submarine surveillance. Beams from multiple hydrophone
arrays are used to detect and locate underwater threats. Recently, SOSUS has been replaced
by the more sophisticated Integrated Undersea Surveillance System.
Networks of air defense radars can be regarded as an example of networked large
scale sensors. Both ground-based radar systems and Airborne Warning and Control System
(AWACS) planes are integrated into such networks to provide all-weather surveillance,
command, control, and communications.
Another early example of sensing with wireless devices is the Air Delivered Seismic
Intrusion Detector (ADSID) system, used by US Air Force in the Vietnam War. Each ADSID
node was about 48 inches in length, nine inches in diameter, and weighted 38 pounds.
2.4 Next Generation Wireless Sensor Nodes
2.4.1 WINS from UCLA
In 1996, the Low Power Wireless Integrated Microsensors (LWIMs) were produced
by UCLA and the Rockwell Science Center. By using commercial, low cost CMOS
fabrication, LWIMs demonstrated the ability to integrate multiple sensors, electronic
interfaces, control, and communication on a single device. LWIM supported over 100 Kbps
wireless communication at a range of 10 meters using a 1 mW transmitter. In 1998, the same
team built a second generation sensor node the Wireless Integrated Network Sensors (WINS).
Commercial WINS from Rockwell Science Center each consists of a processor board with an
Intel Strong Arm SA1100 32-bit embedded processor (1 MB SRAM and 4 MB flash
LITERATURE SURVEY
10
memory), a radio board that supports 100 Kbps with adjustable power consumption from 1 to
100 mW, a power supply board, and a sensor board. These boards are packaged in a
3.5"x3.5"x3" enclosure. The processor consumes 200 mW in the active state and 0.8 mW
when sleeping.
2.4.2 Motes from UC Berkeley
While WINS offer relatively powerful processing and communication capabilities,
other research efforts have been developing smaller and cheaper nodes with less power
consumption. The Mica family was released in 2001, including Mica , Mica2, Mica2Dot, and
MicaZ. While Mica still used an 8-bit 4 MHz microcontroller (ATmega103L), it offered
enhanced capabilities in terms of memory and radio, compared with preceding products.
Mote architecture allowed several different sensor boards, or a data acquisition board, or a
network interface board to be stacked on top of the main processor/radio board. The follow
ups to Mica, Mica2 and Mica2Dot were built in 2002 with an ATmega128L microcontroller
that reduced standby current (33 mW active power and 75 µW sleep power). One year later,
MicaZ was produced with a Chipcon CC2420 wideband radio module that supported
802.15.4 and ZigBee protocols, with a data rate up to 250 Kbps. This radio module also
supported on-chip data encryption and authentication.
The latest member in the family, Telos, was released in 2004. Telos offered a set of
new features: (1) a microcontroller from Texas Instruments with 3 mW active power and
15µW sleep power, (2) an internal antenna built into the printed circuit board to reduce cost,
(3) an on-board USB for easier interface with PCs, (4) integrated humidity, temperature, and
light sensors, and (5) a 64-bit MAC address for unique node identification. The integrated
RAM and flash memory architecture has greatly simplified the design of the mote family.
However, the tiny footprint also requires a specialized operating system, which was
developed by UC Berkeley, called TinyOS. TinyOS features a component-based architecture
and event driven model that are suitable for programming with small embedded devices, such
as motes. The combination of Motes and TinyOS is gradually becoming a popular
experimental platform for many research efforts in the field of WSNs.
LITERATURE SURVEY
11
2.4.3 Medusa from UCLA
The design philosophy and operational space of motes are quite different from those
of WINS. On one hand, motes are designed for simple sensing and signal processing
applications, where the demand for computation and communication capabilities is low. On
the other hand, WINS are essentially an embedded version of PDAs, for more advanced
computationally intensive applications with large memory space requirements. To bridge the
gap between the two extremes, the Medusa MK-2 sensor node was developed by the Center
for Embedded Networked Sensing (CENS) at UCLA in 2002. One distinguishing feature of
Medusa MK-2 is that it integrates two microcontrollers. The first one, ATmega128, is
dedicated to less computationally demanding tasks, including radio base band processing and
sensor sampling. The second one, AT91FR4081, is a more powerful microcontroller (40
MHz, 1 MB flash, 136 KB RAM) that can be used to handle more sophisticated, but less
frequent signal processing tasks (e.g., the Kalman filter). The combination of these two
microcontrollers provides more flexibility in WSN development and deployment, especially
for applications that require both high computation capabilities and long lifetime.
2.5 Why Microscopic Sensor Nodes?The transition from large to small scale sensor nodes has several advantages.
(1) Small sensor nodes are easy to manufacture with much lower cost than large scale
sensors. They are even disposable if the envisioned US$1 target price can be realized in the
future.
(2) With a mass volume of such low cost and tiny sensor nodes, they can be deployed very
closely to the target phenomena or sensing field at an extremely high density. Therefore, the
shorter sensing range and lower sensing accuracy of each individual node are compensated
for by the shorter sensing distance and large number of sensors around the target objects,
which generates a high signal to noise ratio (SNR).
(3) Since computing and communication devices can be integrated with sensors, large-sample
in-network and intelligent information fusion becomes feasible. The intelligence of sensor
nodes and the availability of multiple onboard sensors also enhances the flexibility of the
entire system.
LITERATURE SURVEY
12
(4) Due to their small size and self-contained power supply, sensor nodes can be easily
deployed into regions where replenishing energy is not available, including hostile or
dangerous environments. The survivability of nodes also increases with reduced size.
(5) The high node density enables system-level fault tolerance through node redundancy.
SOFTWARE AND PROGRAMMING DESCRIPTION
SOFTWARE AND PROGRAMMING DESCRIPTION
13
3.1 Introduction
3.1.1 MoteConfig
MoteConfig is a Windows-based GUI (Graphical user interface) utility for
programming Motes. This utility provides an interface for configuring and downloading pre-
compiled XMesh/TinyOS firmware applications onto Motes. MoteConfig allows the user to
configure the Mote ID, Group ID, RF channel and RF power. High-power and low-power
XMesh applications are available for each sensor board and platform manufactured by
Crossbow as part of the MoteView install.
Each Mote has a 512kB external non-volatile flash divided into 4 slots. These slots
have a default size of 128 kB. Slot 0 is reserved for the OTAP (Over-The-Air-Programming).
The Over-The-Air-Programming (OTAP) feature allows users to reprogram a Mote over a
wireless link. Slots 1, 2 and 3 can be used for user-specified firmware.
During the OTAP process, the server sends a command to the Mote to reboot into the OTAP
image (slot 0). A user-specified firmware image is broken up into fragments and transmitted
to the Mote and stored into Slot 1, 2 or 3. The server can send a message to transfer the newly
uploaded firmware into the program flash and reboot the Mote.
3.2 Installation
3.2.1 Supported Platforms
MoteConfig is supported on the following operating systems:
Windows XP Home
Window XP Professional
Windows 2000 with SP4
3.3 Installing MoteView on a Windows PC
Before you can use MoteView you have to install it on a PC. The requirements necessary
to properly install MoteView are below:
1. A PC with one of the following operating systems
Windows XP Home/Professional
Windows 2000 with SP4
2. An NTFS file system.
SOFTWARE AND PROGRAMMING DESCRIPTION
14
3. Screen resolution must be at least 800 × 600 or the interface will require scrollbars.
4. Administrative privileges to write to Windows registry.
5. Prior to installing MoteView, it is highly recommended that you shut down all the
programs running on your computer.
3.3.1 Installation Steps:
1. Insert the MoteWorks Support Tools CDROM into the computer s CD drive.
2. Double-click on MoteView_2.0.F_Setup.exe from MoteView folder.
3. Select the desired installation directory (the default installation directory is C:\Program
Files\Crossbow\MoteView)
4. Select all available installation tasks.
5. Install the following during installation:
MoteView application
PostgreSQL 8.0 database service
PostgreSQL ODBC driver
Microsoft .NET framework
MoteView has four main user interface sections which we can browse and use.
Toolbar / Menus: Allows the user to specify actions and initiate command dialogs.
Node List: Shows all known nodes in a deployment and health status summary.
Visualization Tabs: Enables the user to view the sensor data in various ways.
Server Messages: Displays a log of server events and incoming messages.
3.4 PC Interface Port Requirements
The gateway platform used in the base station determines the PC interface port required
by MoteConfig.
1. For a MIB510 serial gateway: an RS-232 serial port.
2. For a MIB520 USB gateway: a USB port.
SOFTWARE AND PROGRAMMING DESCRIPTION
15
3. For a MIB600 Ethernet gateway: A wired Ethernet or 802.11 wireless card (if the
MIB600 is on a LAN with wireless access).
Table 3.1 Pre-compiled MICAz XMesh applications
MICAz Mote (MPR2400 and MPR2600)
Board Model Binary file name
MDA board
MDA100CA XMDA100CA_2420_<mode>.exe
MDA100CB XMDA100CB_2420_<mode>.exe
XBW-DA100CA XBW-DA100CA_2420_hp.exe
XBW-DA100CB XBW-DA100CB_2420_hp.exe
MDA300 XMDA300_2420_<mode>.exe
MDA300 (precision) XMDA300p_2420_<mode>.exe
MDA320 XMDA320_2420_<mode>.exe
XBW-DA325 XDA325_2420_<mode>.exe
Base Station (common to all boards)XMeshBase_2420_<mode>.exe
<mode> = hp or lp.hp = high power mesh networking. lp = low-power mesh networking.
3.5 Installation Steps for MoteConfig
MoteConfig is shipped as a component of MoteView and MoteWorks:
1. MoteConfig is automatically installed with the MoteView installer.
2. MoteConfig is an optional component in the MoteWorks installer. Make sure that
MoteConfig 2.0 and OTAP item is selected as shown in figure.
SOFTWARE AND PROGRAMMING DESCRIPTION
16
Figure 3.1 MoteConfig 2.0 and OTAP MoteWorks Installer
3.6 Starting MoteConfig
If MoteConfig was installed using the MoteView installer, use the
following steps:
Open MoteView 2.0F by either clicking on the shortcut located on the Desktop, or by
going to Start > Programs > Crossbow > MoteView 2.0F.
• Press the Program Mote button on the MoteView toolbar to spawn the MoteConfig
GUI as shown in figure.
SOFTWARE AND PROGRAMMING DESCRIPTION
17
Figure 3.2 MoteConfig Application GUI
If MoteConfig was installed using the MoteWorks installer
Click on the shortcut located on the Desktop, or select Start > Programs > Crossbow > MoteConfig
2.0.
3.7 Local Programming
The Local Program tab is used to upload firmware onto the Motes via a gateway.
To program motes correctly, set up the hardware as follows:
1. The gateway should be powered and connected to the PC via a serial, USB or Ethernet port.
2. If using the MIB510, the SW2 switch should be in the OFF position.
3. The motes should be firmly attached to the gateway.
SOFTWARE AND PROGRAMMING DESCRIPTION
18
4. The motes should be turned off before the programming.
3.8 Settings
Click on Settings > Interface Board to select the correct gateway and port settings.
The MIB520 virtual COM port drivers will install two sequential ports on the PC. The low-
numbered port is used for programming and the high-numbered port is used for communication.
Figure shows the Interface Board Settings for a MIB520 that has created COM 6 and 7 on the PC.
In this example, COM must be selected as the serial port.
NOTE: The MIB520 requires the installation of the FTDI FT2232C drivers. Once these drivers
are installed, the Device Manager (Start > Control Panel > System > Hardware) will display the
MIB520 as two new virtual com ports.
Figure 3.3 MIB520 Gateway Settings
3.9 Programming
The pre-compiled XMesh applications installed with MoteView are located in C > Program Files
> Crossbow > MoteView > XMesh.
Press the Select button to open a file browser as shown in Figure. Navigate to the folder that
corresponds to your Mote processor/radio board, radio frequency (for micaz and MICA) and
sensor board type.
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19
Figure 3.4 File Browser for selecting XMesh applications
Low-power and high-power applications have been included for most sensor boards.
Note: 1. The MEP and MSP node firmware is located in separate named folders.
2. The base station Mote must be programmed with XMeshBase_2420_<hp or lp>.exe and a
node ID of 0.
After an application has been selected, the binary scan feature built into MoteConfig will display
the default parameters programmed into the application (see Figure).
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Figure 3.5 Binary Scan Result of an XMeshBase application
These default parameters can be overwritten by the user by specifying the desired MOTE ID,
GROUP ID, RF Power, and RF Channel.
NOTE: Remote nodes must be programmed with a non-zero Mote ID.
Press the Program button to download the selected firmware and configuration into the mote, as
shown in Figure.
When programming is complete, the Upload SUCCESSFUL! message is printed in the status
box as shown in Figure.
The Stop button can be used to cancel a firmware download in progress.
SOFTWARE AND PROGRAMMING DESCRIPTION
21
Figure 3.6 MoteConfig programming in progress
Figure 3.7 MoteConfig programming successful
SOFTWARE AND PROGRAMMING DESCRIPTION
22
3.10 MoteViewA mesh network is a generic name for a class of networked embedded systems that share several
characteristics including:
• Multi-Hop -- the capability of sending messages peer-to-peer to a base station, thereby
enabling scalable range extension.
• Self-Configuring -- capable of network formation without human intervention.
• Self-Healing -- capable of adding and removing network nodes automatically without
having to reset the network.
• Dynamic Routing -- capable of adaptively determining the route based on dynamic
network conditions (e.g., link quality, hop-count, gradient, or other metric).
A wireless network deployment is composed of the three distinct software tiers:
• The Client Tier provides the user visualization software and graphical interface for
managing the network. Crossbow provides free client software called MoteView that bundles
software from all three tiers to provide an end-to-end solution.
• The Server Tier is an always-on facility that handles translation and buffering of data from
the wireless network and provides the bridge between the wireless motes and the internet
clients.
• The Mote Tier, where XMesh resides, is the software the runs on the cloud of sensor nodes
forming a mesh network.
3.10.1 MoteView Overview
MoteView is designed to be an interface between a user and a deployed network of wireless
sensors. MoteView provides the tools to simplify deployment and monitoring. It also makes it
easy to connect to a database, to analyze, and to graph sensor readings.
In the three-part framework for deploying a sensor network system, the first part is the Mote layer
or sensor mesh network. The Motes are programmed with XMesh/TinyOS firmware to do a
specific task: e.g., microclimate monitoring, asset tracking, intrusion detection, etc. The second
layer or Server tier provides data logging and database services. At this layer sensor readings
arrive at the base station (e.g., MIB510, MIB520, MIB600, or Stargate) and are stored on a server
or Stargate. The third part is the client tier in which software tools provide visualization,
SOFTWARE AND PROGRAMMING DESCRIPTION
23
monitoring, and analysis tools to display and interpret sensor data. The purpose of this document
is to explain the features of MoteView and to provide information on the supported Mote layer
applications, Mote platforms, and sensor boards.
3.11 Supported Sensor Boards and Mote Platforms
MoteView supports all of Crossbow s sensor and data acquisition boards as well as the
MICA2, MICA2DOT, and MICAz processor/radio platforms.
MOTEPLATFORMS
MODELNUMBER(S)
RF FREQUENCY BAND(S)
IRIS XM2110 2400 MHZ TO 2483.5 MHZM2110 2400 MHZ TO 2483.5 MHZ
MICAZ MPR2400 2400 MHZ TO 2483.5 MHZMPR2600 2400 MHZ TO 2483.5 MHZ
MICA2 MPR400 868 MHZ TO 870 MHZ; 903 MHZ TO 928MHZ
MPR410 433.05 TO 434.8 MHZ
MPR600 868 MHZ TO 870 MHZ; 903 MHZ TO 928MHZ
MICA2DOT MPR510 868 MHZ TO 870 MHZ; 903 MHZ TO 928MHZ
MPR520 433.05 TO 434.8 MHZ
Table 3.2 Mote processor/radio (MPR) platforms supported by MoteView
SOFTWARE AND PROGRAMMING DESCRIPTION
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Sensor and DataAcquisition Boards
Mote Platforms
IRIS MICAZ MICA2 MICA2DOT
MTS101
MTS300/310
MTS410
MTS400/MTS420
MTS450
MTS510
MDA100
XBW-DA100
MDA300
MDA320
XBW-DA325
MDA500
Table 3.3 Sensor (MTS series) and data acquisition boards supported by MoteView and theirplug-and-play compatible Mote platforms
SOFTWARE AND PROGRAMMING DESCRIPTION
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3.12 Application Quick Start
Once a sensor network is running and MoteView is installed on a PC, minimal configuration is
necessary to start collecting data from the sensor network.
Verify PostgreSQL Installation
During the installation of MoteView a static database was included to make it possible to
demonstrate MoteView s features without having to be connected to an active sensor network or
a remote server/database. The steps described here also apply to viewing data collected from an
active sensor network.
3.12.1 Connecting to a Live Sensor Network on your local PC
Use the following steps to access data from a live sensor network connected to your local PC via
the MIB510, MIB520 or MIB600 gateway.
1. Click on the Connect to WSN icon, the Connect to WSN Wizard will appear. Select the
Mode tab, check on Acquire Live Data as operation mode and Local as acquisition type
and click on Next >>.
Figure3.8
SOFTWARE AND PROGRAMMING DESCRIPTION
26
2. In the Gateway tab, specify the Interface Board type, Port/Host Name etc as described
below.
i. If using a MIB510, in the Gateway tab make sure that the MIB510 s COM is set to the
correct port number and that the baud rate is 57600.
Figure 3.9
ii. If using MIB520, enter the higher of the 2 COM ports installed by the MIB520 s driverand set the baud rate to 57600.
Figure 3.10
SOFTWARE AND PROGRAMMING DESCRIPTION
27
NOTE: The MIB520 requires the installation of the FTDI FT2232C drivers. Once these
drivers are installed, the Device Manager (Start > Control Panel > System > Hardware)
will display the MIB520 as two new virtual com ports.
iii. If using a MIB600, select MIB600 from Interface Board dropdown and enter the IP
address of the MIB600 in the Hostname text-box. The Port should default to 10002.
Figure 3.11
3. In the Sensor Board tab, uncheck the View Alternate Table checkbox and choose the
XMesh Application Name that matches the firmware programmed into the Mote from
Application Name dropdown. Click on Done.
SOFTWARE AND PROGRAMMING DESCRIPTION
28
Figure 3.12
4. If you are not able to receive data, you may also need to check the LIVE check box
on the main MoteView screen if it has not been previously checked. Use the Server Messages
pane at the bottom of your MoteView display to verify that node data is being received by
your PC.
3.13 Visualization TabsSeven visualization tabs (Data, Command, Charts, Health, Histogram, Scatter plot and
Topology) provide different methods of viewing your sensor data.
3.13.1 Data
The Data tab displays the latest sensor readings received for each node in the network.
The columns include node ID, server timestamp and sensor values from the sensor board
firmware packet. The sensor data is automatically converted into standard engineering units.
Left-clicking the column header allows you to sort by node ID, parent, temperature,
voltage, last result time, or any other sensor reading. Right-clicking the column header
displays a pop-up menu with unit conversions relevant to the sensor.
SOFTWARE AND PROGRAMMING DESCRIPTION
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Figure 3.13 Screenshot of a demo database displayed in the Data tab
3.14 Summary of the Process How to get live Data through Sensors1. Install MoteView and Moteworks.
2. Install FTDI(Future Technology Devices International Limited) drivers for USB.
3. The programming of motes/nodes is done with the help of
MoteConfig. We will get two communication ports for e.g COM5,
COM6, then select smaller communication port for programming
the motes.
4. Go in settingsàInterface board MIB520à COM5àApply.
5. The file to be uploaded should be gone through path Program files
àCrossbowàMoteViewàXmeshàMicazàMDA100àhp.
6. We have to program the base station or gateway also.
7. We should keep same Group ID for all the network and Node ID
should be for base and non-zero value for the nodes.
8. Then click program after that program will be uploaded in the
nodes, during programming switch off the nodes.
SOFTWARE AND PROGRAMMING DESCRIPTION
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9. Then open MoteView. Connect to WSN.
a. On the gateway, make sure to choose the higher com port number.
b. In the sensor board, choose MTS310 as Application name then
click on view alternate table. Then choose the xbw_da100_results
as the database table name.
c. Turn ON all the nodes. (Put batteries in each of the sensor node
and turn the switch ON).
d. Click on done. You should be able to view data being logged in
from all the nodes.
SENSOR KIT DETAILS (HARDWARE DESCRIPTION)
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31
4.1 Introduction
The hardware features of the Mote Processor Radio (MPR) platforms and Mote
Interface Boards (MIB) for network base stations and programming interfaces. It is intended
for understanding and leveraging Crossbow s Smart Dust hardware design in real-world
sensor network, smart RFID, and ubiquitous computing applications.
4.2 MPR2400 (MICAz)
4.2.1 Product Summary
The MICAz is the latest generation of Motes from Crossbow Technology. The
MPR2400 (2400 MHz to 2483.5 MHz band) uses the Chipcon CC2420, IEEE 802.15.4
compliant, ZigBee ready radio frequency transceiver integrated with an Atmega128L micro-
controller. The same MICA family, 51 pin I/O connector, and serial flash memory is used; all
application software and sensor boards are compatible with the MPR2400.
Figure 4.1 Photo of the MPR2400 MICAz with standard antenna
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32
4.2.2 Block Diagram and Schematics for the MPR2400 / MICAz
Figure 4.2
SENSOR KIT DETAILS (HARDWARE DESCRIPTION)
33
4.2.3 51-pin Expansion Connector
Figure 4.3(a)
Figure 4.3(b)
SENSOR KIT DETAILS (HARDWARE DESCRIPTION)
34
4.2.4 CC2420 Radio
Figure 4.4
SENSOR KIT DETAILS (HARDWARE DESCRIPTION)
35
4.3 Data Acquisition Boards
The MTS series of sensor boards and MDA series of sensor/data acquisition boards
are designed to interface with Crossbow s MICA, MICA2, and MICA2DOT family of
wireless Motes. There are a variety of sensor boards available, and the sensor boards are
specific to the MICA, MICA2 board or the MICA2DOT form factor. The sensor boards allow
for a range of different sensing modalities as well as interface to external sensor via
prototyping areas or screw terminals.
Crossbow Part Name Motes Supported Sensors and Features
MTS101CA MICAz, MICA2, MICA Light, temperature, prototyping area
MTS300CAIRIS, MICAz, MICA2, MICA Light, temperature, microphone, and
buzzerMTS300CB
MTS510CA MICA2DOT Light, microphone, and 2-axisaccelerometer
MDA100CAIRIS, MICAz, MICA2 Light, temperature, prototyping area
MDA100CB
MDA300CA IRIS, MICAz, MICA2 Light, relative humidity, general purposeinterface for external sensors
MDA500CA MICA2DOT Prototyping area
Table 4.1 Crossbow s Sensor and Data Acquisition Boards
4.4 MDA100CA/MDA100CB
MDA100CA and MDA100CB have the same content except for some minor changes.
The MDA100 series sensor boards have a precision thermistor, a light sensor/photocell, and
general prototyping area. The prototyping area supports connection to all eight channels of
SENSOR KIT DETAILS (HARDWARE DESCRIPTION)
36
the Mote s analog to digital converter (ADC0 7), both USART serial ports and the I2C
digital communications bus. The prototyping area also has 45 unconnected holes that are used
for breadboard of circuitry.
4.4.1 Thermistor
The thermistor, sensor is a highly accurate and highly stable sensor element. With
proper calibration, an accuracy of 0.2 °C can be achieved. The thermistor s resistance varies
with temperature. The resistance vs. temperature graph is non-linear. The sensor is connected
to the analog-digital converter channel number 1 (ADC1) thru a basic resistor divider circuit.
The sensor is connected to the analog-digital converter channel number 1 (ADC1) through a
basic resistor divider circuit.
Temperature (°C) Resistance (Ohms)
-40 239,800
-20 78,910
0 29,940
25 10,000
40 5592
60 2760
Table 4.2 Resistance vs. Temperature
4.4.2 Conversion to Engineering Units
The Mote s ADC output can be converted to Kelvin using the following
approximation over 0 to 50 °C:
SENSOR KIT DETAILS (HARDWARE DESCRIPTION)
37
1/T(K) = a + b × ln(Rthr
) + c × [ln(Rthr
)]3
where:
Rthr
= R1(ADC_FS-ADC)/ADC
a = 0.001010024
b = 0.000242127
c = 0.000000146
R1 = 10 k
ADC_FS = 1023, and
ADC = output value from Mote s ADC measurement.
4.4.3 Light Sensor
The light sensor is a simple CdSe photocell. The maximum sensitivity of the photocell
is at the light wavelength of 690 nm. Typical on resistance, while exposed to light, is 2 k . In
order to use the light sensor, digital control signal PW1 must be turned on. The output of the
sensor is connected to the analog-digital converter channel 1 (ADC1). When there is light, the
nominal circuit output is near VCC or full-scale, and when it is dark the nominal output is
near GND or zero.
4.4.4 Prototyping Area
The prototyping area is a series of solder holes and connection points for connecting
other sensors and devices to the Mote.
SENSOR KIT DETAILS (HARDWARE DESCRIPTION)
38
A B C D E F
1 GND GND GND VCC VCC VCC
2 OPEN OPEN USART1_CK INT3 ADC2 PW0
3 OPEN OPEN UART0_RX INT2+ ADC1+ PW1+
4 OPEN OPEN UART0_TX INT1 ADC0+ PW2
5 OPEN OPEN SPI_SCK INT0 THERM_PWR PW3
6 OPEN OPEN USART1_RX BAT_MON THRU1 PW4
7 OPEN OPEN USART1_TX LED3 THRU2 PW5
8 OPEN OPEN I2C_CLK LED2 THRU3 PW6
9 OPEN OPEN I2C_DATA LED1 RSTN ADC7
10 OPEN OPEN PWM0 RD PWM1B ADC6
11 OPEN OPEN PWM1A WR OPEN ADC5
12 OPEN OPEN AC+ ALE OPEN ADC4
13 OPEN OPEN AC- PW7 OPEN ADC3
14 GND GND GND VCC VCC VCC
15 OPEN OPEN OPEN OPEN OPEN OPEN
16 OPEN OPEN OPEN OPEN OPEN OPEN
17 OPEN OPEN OPEN OPEN OPEN OPEN
Table 4.3 Connection Table for MDA100. Use the photo (top view) below the table to
locate the pins.
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39
Figure 4.5 MDA100CB
WARNING: Never connect signals that are greater than VCC (3V typical) or less than 0 V
to any of the holes that connect to the Mote Processor Radio board. It is okay to connect
different voltages to the non-connected holes. However, be careful. If a voltage out of the
range of 0 to Vcc should reach the Mote Processor Radio Board damage will occur.
4.5 MIB520 USB Interface Board
The MIB520 provides USB connectivity to the IRIS and MICA family of Motes for
communication and in-system programming. It supplies power to the devices through USB
bus. MIB520CB has a male connector while MIB520CA has female connector.
Figure 4.6 Photo of top view of an MIB520CA
SENSOR KIT DETAILS (HARDWARE DESCRIPTION)
40
Figure 4.7 Photo of top view of an MIB520CB
4.5.1 ISP (In System Processor)
The MIB520 has an on-board in-system processor (ISP) an Atmega16L located at
U14 to program the Motes. Code is downloaded to the ISP through the USB port. Next the
ISP programs the code into the Mote.
4.5.2 Mote Programming Using the MIB520
Programming the Motes requires having MoteWorks/TinyOS installed in your host
PC. The IRIS, MICAz and MICA2 Motes connect to the MIB520 for UISP programming
from USB connected host PC.
4.6 MIB520 Use
4.6.1 Install FTDI USB Virtual COM Port Drivers
MIB520 uses FTDI FT2232C to use USB port as virtual COM port. Hence we need to
install FT2232C VCP drivers.
• When you plug a MIB520 into your PC for the first time, the Windows detects and
reports it as a new hardware. Please select Install from a list or specific location
SENSOR KIT DETAILS (HARDWARE DESCRIPTION)
41
(Advanced) and browse to MIB520 Drivers folder of the WSN Kit CDROM.
Install shield wizard will guide you through the installation process.
• When the drivers are installed, you will see two serial ports added under the Control
PanelàSystemàHardwareàDevice ManageràPort. Make a note of the assigned
COM port numbers.
• The two virtual serial ports for MIB520 are Comn and Com(n+1); Comn is for Mote
programming and Com(n+1) is for Mote communication.
4.6.2 Reset
The RESET push button switch resets both the ISP and Mote processors. It also
resets the monitoring software which runs on the host PC.
4.6.3 JTAG
The MIB520 has a connector, J3 which connects to an Atmel JTAG pod for in-circuit
debugging. This connector will supply power to the JTAG pod; no external power supply is
required for the pod.
WARNING: The MIB520 also has JTAG and ISP connectors for the ISP processor. These
are for factory use only.
4.6.4 Power
The MIB520 is powered by the USB bus of the host.
WARNING: When programming an IRIS/MICAz/MICA2 with the MIB520, turn off the
battery switch.
4.6.5 USB Interface
The MIB520 offers two separate ports: one dedicated to in-system Mote programming
and a second for data communication over USB.
FUNDAMENTALS OF CLUSTER BASED ROUTING
FUNDAMENTALS OF CLUSTER BASED ROUTING
42
5.1 Introduction
Hierarchical cluster-based routing scheme is suitable for habitat and environmental
monitoring applications. The routing scheme is based on the fact that the energy consumed to
send a message to a distant node is far greater than the energy needed for a short range
transmission. We extend the LEACH protocol by using a head-set instead of a cluster head.
In other words, during each election, a head-set that consists of several nodes is selected. The
members of a head-set are responsible for transmitting messages to the distant base station.
At one time, only one member of the head-set is active and the remaining head-set members
are in sleep mode. The task of transmission to the base station is uniformly distributed among
all the head-set members.
First, we describe a few terms that are used in defining our protocol. A clusterhead is
a sensor node that transmits an aggregated sensor data to the distant base station. Non-cluster
heads are sensor nodes that transmit the collected data to their cluster head. Each cluster has a
head-set that consists of several virtual cluster heads; however, only one head-set member is
active at one time. Iteration consists of two stages: an election phase and a data transfer
phase. In an election phase, the head-sets are chosen for the pre-determined number of
clusters. In the data transfer phase, the members of head-set transmit aggregated data to the
base station. Each data transfer phase consists of several epochs. Each member of a head-set
becomes a cluster head once during an epoch. A round consists of several iterations. In one
round, each sensor node becomes a member of head-set for one time.
5.2 States of a sensor node
The damaged or malfunctioning sensor states are not considered. Each sensor node
joins the network as a candidate. At the start of each iteration, a fixed number of sensor nodes
are chosen as cluster heads; these chosen cluster heads acquire the active state. By the end of
election phase, a few nodes are selected as members of the head-sets; these nodes acquire
associate state. At the end of an election phase, one member of a head-set is in active state
and the remaining head-set members are in associate state.
In an epoch of a data transfer stage, the active sensor node transmits a frame to the
base station and goes into the passive associate state. Moreover, the associate, which is the
next in the schedule to transmit to the base station, acquires the active state. During an epoch,
the head-set members are distributed as follows: one member is in active state, a few
members are in associate state, and a few members are in passive associate state.
FUNDAMENTALS OF CLUSTER BASED ROUTING
43
During the transmission of the last frame of an epoch, one member is active and the
remaining members are passive associates; there is no member in an associate state. Then, at
the start of the next epoch, all the head-set members become associate and one of them is
chosen to acquire the active state. At the end of an iteration, all the head-set members acquire
the non-candidate state. The members in non-candidate state are not eligible to become a
member of an head-set. At the start of a new round, all non-candidate sensor nodes acquire
candidate state; a new round starts when all the nodes acquire non-candidate state.
5.3 Election Phase
In the proposed model, the number of clusters, k, are pre-determined for the wireless
sensor network. At the start, a set of cluster heads are chosen on random basis. These cluster
heads send a short range advertisement broadcast message. The sensor nodes receive the
advertisements and choose their cluster heads based on the signal strengths of the
advertisement messages. Each sensor node sends an acknowledgment message to its cluster
head. Moreover, for each iteration, the cluster heads choose a set of associates based on the
signal analysis of the acknowledgments.
A head-set consists of a cluster head and the associates. The head-set, which is
responsible to send messages to the base station, is chosen for one iteration of a round. In an
epoch of an iteration, each member of the headset becomes a cluster head. All the head-set
members share the same time slot to transmit their frames. Based on uniform rotation, a
schedule is created for the head-set members for their frame transmissions; only the active
cluster head transmits a frame to the base station. Moreover, a schedule is created for the data
acquisition and data transfer time intervals for the sensor nodes that are not members of the
head-set.
5.4 Data Transfer Phase
Once clusters, head-sets, and TDMA-based schedules are formed, data transmission
begins. The non-cluster head nodes collect the sensor data and transmit the data to the cluster
head, in their allotted timer slots. The cluster-head node must keep its radio turned on to
receive the data from the nodes in the cluster. The associate members of the head-set remain
in the sleep mode and do not receive any messages. After, some pre-determined time interval,
the next associate becomes a cluster head and the current cluster head becomes a passive
FUNDAMENTALS OF CLUSTER BASED ROUTING
44
head-set member. At the end of an epoch, all the head-set members have become a cluster
head for once. There can be several epochs in an iteration. At the end of an iteration, the
head-set members become non-candidate members and a new head-set is chosen for the next
iteration. Finally, at the end of a round, all the nodes have become non-candidate members.
At this stage, a new round is started and all the nodes become candidate members.
5.5 Quantitative Analysis
In this section, we describe a radio communication model that is used in the
quantitative analysis of our protocol. The energy dissipation, number of frames, time for
message transfer, and the optimum number of clusters are analytically determined.
5.5.1 Radio Communication Model
We use a radio model, where for a shorter distance transmission, such as within
clusters, the energy consumed by a transmit amplifier is proportional to r2. However, for a
longer distance transmission, such as from a cluster head to the base station, the energy
consumed is proportional to r4. Using the given radio model, the energy consumed to transmit
an l-bit message for a longer distance, d, is given by:
4dllelETE ε+= (5.1)
Similarly, the energy consumed to transmit an l-bit message for a shorter distance isgiven by:
2dslelETE ε+= (5.2)
Moreover, the energy consumed to receive the l-bit message is given by:
BFlEelERE += (5.3)
Equation 5.3 includes the cost of beam forming approach that reduces energy
consumption. The constants used in the radio model are given in Table 5.1.
FUNDAMENTALS OF CLUSTER BASED ROUTING
45
Description Symbol Value
Energy consumed by the amplifier to transmitat a shorter distance s 10 pJ/bit/m2
Energy consumed by the amplifier to transmitat a longer distance
l
0.0013pJ/bit/m4
Energy consumed in the electronics circuit totransmit or receive the signal Ee 50 nJ/bitEnergy consumed for beam forming
EBF 5 nJ/bit
Table 5.1 Sample parameter values of the radio communication model used in ourquantitative analysis.
5.5.2 Election Phase
For a sensor network of n nodes, the optimal number of clusters is given as k. All
nodes are assumed to be at the same energy level at the beginning. The amount of consumed
energy is same for all the clusters. At the start of the election phase, the base station randomly
selects a given number of cluster heads. First, the cluster heads broadcast messages to all the
sensors in their neighborhood. Second, the sensors receive messages from one or more cluster
heads and choose their cluster head using the received signal strength. Third, the sensors
transmit their decision to their corresponding cluster heads. Fourth, the cluster heads receive
messages from their sensor nodes and remember their corresponding nodes. For each cluster,
the corresponding cluster head chooses a set of m associates, based on signal analysis.
For uniformly distributed clusters, each cluster contains n/k nodes. Using Equation
5.2 and Equation 5.3, the energy consumed by a cluster head is estimated as follows:
{ }
+−++=− )()1(2
BFEeElkndslelEelecCHE ε (5.4)
The first part of Equation 5.4 represents the energy consumed to transmit the
advertisement message; this energy consumption is based on a shorter distance energy
dissipation model. The second part of Equation 5.4 represents the energy consumed to
receive )1( −kn messages from the sensor nodes of the same cluster.
FUNDAMENTALS OF CLUSTER BASED ROUTING
46
Using Equation 5.2 and Equation 5.3, the energy consumed by non-cluster head
sensor nodes is
estimated as follows:
{ } { }2dslelEBFklEeklEelecCHnonE ε+++=−− (5.5)
The first part of Equation 5.5 shows the energy consumed to receive messages from k
cluster heads; it is assumed that a sensor node receives messages from all the cluster heads.
The second part of Equation 5.5 shows the energy consumed to transmit the decision to the
corresponding cluster head.
5.5.3 Data Transfer Phase
During data transfer phase, the nodes transmit messages to their cluster head and
cluster heads transmit an aggregated messages to a distant base station. The energy consumed
by a cluster head is as follows:
{ }
+−++= )()(4
/ BFEeElmkndllelEframeCHE ε (5.6)
The first part of Equation 5.6 shows the energy consumed to transmit a message to the
distant base station. The second part of Equation 5.6 shows the energy consumed to receive
messages from the remaining )( mkn
− nodes that are not part of the head-set.
The energy, Enon-CH/frame, consumed by a non-cluster head node to transmit the sensor
data to the cluster head is given below:
2/ dslelEframeCHnonE ε+=− (5.7)
For circular clusters with a uniform distribution of sensor nodes and a network
diameter of M, the average value of d2 is given as: E[d2] = )2
2
kM( . Equation 5.7 can be
simplified as follows:
kM
slelEframeCHnonEΠ
+=− 2
2/ ε (5.8)
In first iteration, Nf data frames are transmitted. The frames transmitted by each
cluster are Nf /k. The Nf /k frames are uniformly divided among n/k nodes of the cluster. Each
FUNDAMENTALS OF CLUSTER BASED ROUTING
47
cluster head frame transmission needs )( mkn
− non-cluster head frames. For simplification of
equations, the fractions f1 and f2 are given as below:
kmknf 1
1
11
+−= (5.9)
kmkn
mkn
f 1
12
+−
−= (5.10)
The energy consumptions in a data transfer stage of each cluster are as follows:
frameCHEfNfdataCHE /1=− (5.11)
frameCHnonEfNfdataCHnonE /2 −=−− (5.12)
RESULTS AND DISCUSSION
RESULTS AND DISCUSSION
48
RESULTS
1. Result showing the temperature and light measurements of the surrounding
environment near the nodes. The results are obtained for five different nodes placed at
different locations by using Crossbow sensor kit.
Figure 6.1 Showing temperature and light measurement
RESULTS AND DISCUSSION
49
2. Result showing the graphical (topology) representation of temperature measurement
of five different nodes at different locations.
Figure 6.2 Showing temperature readings in topology form
RESULTS AND DISCUSSION
50
3. Result showing the graphical (topology) representation of light measurement of five
different nodes at different locations.
Figure 6.3 Showing light readings in topology form
RESULTS AND DISCUSSION
51
4. Result showing the graphical (topology) representation of voltage measurement of
five different nodes at different locations.
Figure 6.4 Showing voltage readings in topology form
RESULTS AND DISCUSSION
52
5. The graph that shows the variation in optimum number of clusters with respect to the
head-set size, where the base station is at distance=150m and the number of nodes n=1000.
The head-set size can be varied between 1 and 6. As the graph shows, the head-set size
cannot be greater than 6. Moreover, for a given head-set size, the maximum number of
clusters can also be determined from the graph.
Figure 6.5 Optimum number of cluster Vs Head set size
1 2 3 4 5 6 7 8 9 100
5
10
15
head set size
optim
um n
o. o
f clu
ster
s
RESULTS AND DISCUSSION
53
6. Graph shows the variation in maximum cluster size with respect to distance from the
base station and the head-set size. As the graph shows, bigger cluster sizes can be managed
for larger values of head-set sizes. However, when the head-set size is small, only small
number of clusters are possible. Moreover, when the distance from the base station is
increased, more energy is spent for a distant transmission. As a result, for the same head-set
size, the maximum number of clusters decreases when the distance to the base station
increases.
Figure 6.6 Variation in maximum cluster size with respect to distance from the base station
and the head-set size.
12
34
5
150
200
2500
2
4
6
8
10
Head-Set SizeDistance
# of
Clu
ster
s
RESULTS AND DISCUSSION
54
7. Graph shows the energy consumption with respect to the number of clusters. As
expected, the energy consumption is reduced when the number of clusters are increased.
However, the rate of reduction in energy consumption is reduced for higher cluster sizes.
Moreover, the energy consumption is lower when head-set size is 3 as compared to head-set
size of 1.
Figure 6.7 The energy consumption with respect to the number of clusters
0 2 4 6 8 10 12 14 16 18 200
1
2
3
4
5
6
Number Of Clusters
Ene
rgy
(J)
RESULTS AND DISCUSSION
55
8. Graph shows the variation in the energy consumed per node with respect to the
number of clusters and network diameter. The x-axis and y-axis represent the number of
clusters and the energy consumed in one round, respectively. In a round, the number of
frames transmitted by one node is 20. The graphs show that energy consumption is reduced
when the number of clusters are increased. For the simulated network of 1000 nodes, graphs
shows that the optimum range of clusters lies between 20 and 60. When the number of
clusters are below the optimum range, for example 10, the data collecting sensor nodes have
to send data to the distant cluster heads. On the other hand, when the number of clusters are
greater than optimum range, there will be more transmissions to the distant base station.
Moreover, the energy consumption is lower for the higher head-set size. In the given graphs,
the energy consumed is approximately three times less when headset size is 3 as compared to
LEACH, where head-set size is 1.
Figure 6.8 Energy consumed per round with respect to number of clusters
050
100150
200
0
20
40
600
0.2
0.4
0.6
0.8
1
Network Diameter (m)# Of Clusters
Est
art (
J)
RESULTS AND DISCUSSION
56
9. Graph shows the variation in the energy consumed per round with respect to head-set
size and network diameter. The x-axis, y-axis, and z-axis represent the network diameter, the
head-set size, and the energy consumed in one round, respectively. The number of data
frames in one iteration is Nf =10,000 and the number of clusters k = 50. As expected, the
graph shows that energy consumption is reduced when the head-set size is increased.
Moreover, this protocol provides a more systematic approach of reducing the energy
consumption. If more nodes are added in LEACH, all the nodes are treated alike and these
extra nodes will also be used in collecting the sensor data. However, in our approach, the
number of sensor nodes for data collection remains unchanged and the number of control and
management nodes can be adjusted.
Figure 6.9 Energy consumed per round with respect to head-set size and networkdiameter.
050
100150
200
0
510
1520
0
0.02
0.04
0.06
0.08
0.1
0.12
Network Diameter (m)Head-Sest Size
Est
art (
J)
RESULTS AND DISCUSSION
57
10. In this section, average time to complete one iteration such that every node becomes a
member of head-set is estimated. In other words, an average time for one iteration in each
round is estimated. Moreover, frames transmitted in each iteration are also evaluated.
The graph shows the variation in time to complete one iteration with respect to cluster
diameter and head-set size. The x-axis, y-axis, and z-axis represent the cluster diameter,
head-set size, and time to complete one iteration, respectively. The head-set size is given as a
percentage of cluster size. The start energy, Estart is fixed for all the cases. The start energy
can be used for the longest period of time when the head-set size is 50% of the cluster size.
When the headset size is less than 50% of the cluster size, there are fewer transmissions in
each iteration but there are more iterations to complete the round. However, when the head-
set size is greater than 50% of the cluster size, there are more transmissions in each iteration,
although there are fewer iterations.
Figure 6.10 The time for iteration with respect to cluster diameter and the head-set size
050
100150
200
0
50
1000
0.5
1
1.5
2
x 104
Network DiameterHead-Set Size (%)
Tim
e fo
r one
iter
atio
n (s
ec)
CONCLUSION AND FUTURE WORK
CONCLUSION AND FUTURE WORK
58
CONCLUSION AND FUTURE WORK
Unlike other networks, WSNs are designed for specific applications. Applications
include, but are not limited to, environmental monitoring, industrial machine monitoring,
surveillance systems, and military target tracking. Each application differs in features and
requirements. The measurement of temperature and light parameters through Crossbow
Sensor Kit by using MoteView and MoteConfig environment has been done. The sensors or
nodes are placed at different locations and the environmental parameters of that locations are
measured. TinyOS is a very extensive and complex system. It has many applications and
tools that need to be studied before one can fully understand the entire system. The results of
our quantitative analysis of the proposed hierarchical cluster-based routing protocol indicate
that the energy consumption can be systematically decreased by including more sensors in a
head-set. For the same number of data collecting sensor nodes, the number of control and
management nodes can be adjusted according to the network environment. In future work,
the variation in the head-set size for different network conditions will be investigated. This
work will be extended to incorporate non-uniform cluster distributions.
REFERENCES
58
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